数控车削中成本最低的切削参数优化方法

Abstract

为选择合理的数控加工切削参数,以最小化加工成本,提出了基于边缘分布估计算法和车削次数枚举方法相结合的新型优化算法。在大量的加工约束条件下,同时优化粗、精两个车削加工阶段的切削参数,进而引入车削成本的理论下限,利用该理论下限不仅可以提高算法的搜索效率,还能评价优化结果。计算机模拟表明,该算法能够找到更优的车削参数组合,从而进一步节约加工成本,且具有更高的运算效率。

To select reasonable cutting parameters of Computer Numerical Control(CNC) machining so as to minimize production cost,a novel optimization approach combined Univariate Marginal Distribution Algorithm(UMDA) with Pass Enumerating method(PE) was proposed.Under lots of machining constraints,the cutting parameters of both rough machining and finish machining were optimized simultaneously.Furthermore,the theoretical lower bounds on Unit production Cost(UC) were introduced,which could be used not only to improve the efficiency of the proposed approach,but also evaluate the optimization results.Computer simulation showed that the proposed UMDA-PE approach could achieve more optimal solution than other approaches proposed previously to reduce UC,and improve the computational efficiency as well.